ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-8-6341-2008Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversionMeirinkJ. F.15BergamaschiP.2KrolM. C.1341Institute for Marine and Atmospheric research Utrecht (IMAU), University of Utrecht, Utrecht, The Netherlands2European Commission Joint Research Centre, Institute for Environment and Sustainability (EC JRC IES), Ispra (VA), Italy3Wageningen University and Research Centre (WUR), Wageningen, The Netherlands4Netherlands Institute for Space Research (SRON), Utrecht, The Netherlands5now at: Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands0511200882163416353This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from http://www.atmos-chem-phys.net/8/6341/2008/acp-8-6341-2008.htmlThe full text article is available as a PDF file from http://www.atmos-chem-phys.net/8/6341/2008/acp-8-6341-2008.pdf

A four-dimensional variational (4D-Var) data assimilation system for inverse modelling
of atmospheric methane emissions is presented. The system is based on the TM5
atmospheric transport model. It can be used for assimilating large volumes of
measurements, in particular satellite observations and quasi-continuous in-situ observations,
and at the same time it enables the optimization of a large number of model parameters,
specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in
posterior emissions. Here, the system is applied to optimize monthly methane emissions
over a 1-year time window on the basis of surface observations from the
NOAA-ESRL network. The results are rigorously
compared with an analogous inversion by Bergamaschi et al. (2007), which was based
on the traditional synthesis approach. The posterior emissions
as well as their uncertainties obtained in both inversions show
a high degree of consistency. At the same time
we illustrate the advantage of 4D-Var in reducing aggregation errors by
optimizing emissions at the grid scale of the transport model.
The full potential of the assimilation system is exploited in
Meirink et al. (2008), who use satellite observations of column-averaged methane
mixing ratios to optimize emissions at high spatial resolution,
taking advantage of the zooming capability of the TM5 model.